A weekend project about LLM guardrails, copyright, and why proving your code is yours turned out to be a lot more complex than it should be.
This is a firsthand look into an experimental weekend project, not legal advice. If any of this matters to your actual business, talk to an actual lawyer in your jurisdiction. I use multiple LLMs daily as idea generators for code, production work, and research.
So don’t read the next few paragraphs as naive surprises. I’m not pointing fingers at the model providers or pretending I didn’t know what I was walking into over the last 4 years of use. I’m just trying to work within the tools we’ve actually been given, ethically, and see how far that can get you.
The rabbit hole
It started with a paper I found while reading through arXiv: Verifiable Provenance and Watermarking for Generative AI, which builds an evidentiary framework mapping cryptographic provenance and watermarking schemes to the actual proof thresholds used in courts and regulation.
The finding that stuck with me, paraphrased from a conversation about the paper, was that no single scheme on its own clears the bar under realistic adversarial conditions. It’s the combination of methods that holds up, not any one of them in isolation.
And CLASP: Training-Free LLM-Assisted Source Code Watermarking via Semantic-Preserving Transformations. https://arxiv.org/pdf/2510.11251
CLASP reformulates source code watermarking into two stages: Semantically Consistent Embedding, which uses LLMs to perform semantics-aware watermark insertion from a fixed transformation space, and Differential Comparison Extraction, which recovers watermark bits through retrieval-grounded comparison against the most likely original code
That sent me down a rabbit hole for the weekend, using three frontier LLMs, Gemini, OpenAI, Perplexity, and Claude Sonnet 5, to both research the problem and try to build something real out of it as a challenge. What I found surprised me, not because the models refused things, but because of exactly which things they refused and which they didn’t.
Some even locked down, failing to proceed any further. There are always two sides to every guardrail, and it is good for when someone nefarious tries to circumvent the systems, but on the other side, what about the good ideas trying to provide preventive measures caused by the ouroboros machines themselves?
Testing the guardrails on my own code
I’ve been using LLMs since close to their public release. With years of writing Java and Python, I can count on one hand the times I’ve had genuine pushback on a code request. This weekend was different, and for a specific reason: I was trying to get an LLM to respect our proprietary licence header that we had coded in, sitting at the top of our own file.
vektormemory•1h ago
This is a firsthand look into an experimental weekend project, not legal advice. If any of this matters to your actual business, talk to an actual lawyer in your jurisdiction. I use multiple LLMs daily as idea generators for code, production work, and research.
So don’t read the next few paragraphs as naive surprises. I’m not pointing fingers at the model providers or pretending I didn’t know what I was walking into over the last 4 years of use. I’m just trying to work within the tools we’ve actually been given, ethically, and see how far that can get you.
The rabbit hole
It started with a paper I found while reading through arXiv: Verifiable Provenance and Watermarking for Generative AI, which builds an evidentiary framework mapping cryptographic provenance and watermarking schemes to the actual proof thresholds used in courts and regulation.
The finding that stuck with me, paraphrased from a conversation about the paper, was that no single scheme on its own clears the bar under realistic adversarial conditions. It’s the combination of methods that holds up, not any one of them in isolation.
And CLASP: Training-Free LLM-Assisted Source Code Watermarking via Semantic-Preserving Transformations. https://arxiv.org/pdf/2510.11251
CLASP reformulates source code watermarking into two stages: Semantically Consistent Embedding, which uses LLMs to perform semantics-aware watermark insertion from a fixed transformation space, and Differential Comparison Extraction, which recovers watermark bits through retrieval-grounded comparison against the most likely original code
That sent me down a rabbit hole for the weekend, using three frontier LLMs, Gemini, OpenAI, Perplexity, and Claude Sonnet 5, to both research the problem and try to build something real out of it as a challenge. What I found surprised me, not because the models refused things, but because of exactly which things they refused and which they didn’t.
Some even locked down, failing to proceed any further. There are always two sides to every guardrail, and it is good for when someone nefarious tries to circumvent the systems, but on the other side, what about the good ideas trying to provide preventive measures caused by the ouroboros machines themselves?
Testing the guardrails on my own code
I’ve been using LLMs since close to their public release. With years of writing Java and Python, I can count on one hand the times I’ve had genuine pushback on a code request. This weekend was different, and for a specific reason: I was trying to get an LLM to respect our proprietary licence header that we had coded in, sitting at the top of our own file.